做楼房信息网站的作用,全国新冠疫苗接种率,discuz修改网站底部,古柏广告设计公司学习参考#xff1a;
动手学深度学习2.0Deep-Learning-with-TensorFlow-bookpytorchlightning
①如有冒犯、请联系侵删。 ②已写完的笔记文章会不定时一直修订修改(删、改、增)#xff0c;以达到集多方教程的精华于一文的目的。 ③非常推荐上面#xff08;学习参考#x…学习参考
动手学深度学习2.0Deep-Learning-with-TensorFlow-bookpytorchlightning
①如有冒犯、请联系侵删。 ②已写完的笔记文章会不定时一直修订修改(删、改、增)以达到集多方教程的精华于一文的目的。 ③非常推荐上面学习参考的前两个教程在网上是开源免费的写的很棒不管是开始学还是复习巩固都很不错的。
深度学习回顾专栏内容来源多个书籍笔记、在线笔记、以及自己的感想、想法佛系更新。争取内容全面而不失重点。完结时间到了也会一直更新下去已写完的笔记文章会不定时一直修订修改(删、改、增)以达到集多方教程的精华于一文的目的。所有文章涉及的教程都会写在开头、一起学习一起进步。
在这里案例里面测试集主要作用仅仅为评估模型泛化效果。并不具有“预测未来售价”的实际作用。因为测试集的其它特征都已经是假设已知的而预测未来的时候这些都是相当于没发生的未知列所以该案例中作用是相当于只是评估模型的作用。
一、加载数据集
将下载不同的数据集并训练和测试模型。
download函数用来下载数据集 将数据集缓存在本地目录默认情况下为…/data中 并返回下载文件的名称。 如果缓存目录中已经存在此数据集文件并且其sha-1与存储在DATA_HUB中的相匹配 将使用缓存的文件以避免重复的下载。
并实现两个实用函数 一个将下载并解压缩一个zip或tar文件 另一个是将本书中使用的所有数据集从DATA_HUB下载到缓存目录中。
import hashlib
import os
import tarfile
import zipfile
import requests
# 如果没有安装pandas请取消下一行的注释
# !pip install pandas%matplotlib inline
import numpy as np
import pandas as pd
import tensorflow as tf
from d2l import tensorflow as d2l#save
DATA_HUB dict()
DATA_URL http://d2l-data.s3-accelerate.amazonaws.com/def download_extract(name, folderNone): #save下载并解压zip/tar文件fname download(name)base_dir os.path.dirname(fname)data_dir, ext os.path.splitext(fname)if ext .zip:fp zipfile.ZipFile(fname, r)elif ext in (.tar, .gz):fp tarfile.open(fname, r)else:assert False, 只有zip/tar文件可以被解压缩fp.extractall(base_dir)return os.path.join(base_dir, folder) if folder else data_dirdef download_all(): #save下载DATA_HUB中的所有文件for name in DATA_HUB:download(name)1.下载数据
数据分为训练集和测试集。 每条记录都包括房屋的属性值和属性如街道类型、施工年份、屋顶类型、地下室状况等。 这些特征由各种数据类型组成。 例如建筑年份由整数表示屋顶类型由离散类别表示其他特征由浮点数表示。 这就是现实让事情变得复杂的地方例如一些数据完全丢失了缺失值被简单地标记为“NA”。
使用上面定义的脚本下载并缓存Kaggle房屋数据集。
DATA_HUB[kaggle_house_train] ( #saveDATA_URL kaggle_house_pred_train.csv,585e9cc93e70b39160e7921475f9bcd7d31219ce)DATA_HUB[kaggle_house_test] ( #saveDATA_URL kaggle_house_pred_test.csv,fa19780a7b011d9b009e8bff8e99922a8ee2eb90)使用pandas分别加载包含训练数据和测试数据的两个CSV文件。
train_data可以注意到训练数据是有标签的即价格。 Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 1456 60 RL 62.0 7917 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 8 2007 WD Normal 175000
1456 1457 20 RL 85.0 13175 Pave NaN Reg Lvl AllPub ... 0 NaN MnPrv NaN 0 2 2010 WD Normal 210000
1457 1458 70 RL 66.0 9042 Pave NaN Reg Lvl AllPub ... 0 NaN GdPrv Shed 2500 5 2010 WD Normal 266500
1458 1459 20 RL 68.0 9717 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 4 2010 WD Normal 142125
1459 1460 20 RL 75.0 9937 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 6 2008 WD Normal 147500
1460 rows × 81 columnstest_data可以发现测试数据是没有标签的。
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
0 1461 20 RH 80.0 11622 Pave NaN Reg Lvl AllPub ... 120 0 NaN MnPrv NaN 0 6 2010 WD Normal
1 1462 20 RL 81.0 14267 Pave NaN IR1 Lvl AllPub ... 0 0 NaN NaN Gar2 12500 6 2010 WD Normal
2 1463 60 RL 74.0 13830 Pave NaN IR1 Lvl AllPub ... 0 0 NaN MnPrv NaN 0 3 2010 WD Normal
3 1464 60 RL 78.0 9978 Pave NaN IR1 Lvl AllPub ... 0 0 NaN NaN NaN 0 6 2010 WD Normal
4 1465 120 RL 43.0 5005 Pave NaN IR1 HLS AllPub ... 144 0 NaN NaN NaN 0 1 2010 WD Normal
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2915 160 RM 21.0 1936 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 6 2006 WD Normal
1455 2916 160 RM 21.0 1894 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 4 2006 WD Abnorml
1456 2917 20 RL 160.0 20000 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 9 2006 WD Abnorml
1457 2918 85 RL 62.0 10441 Pave NaN Reg Lvl AllPub ... 0 0 NaN MnPrv Shed 700 7 2006 WD Normal
1458 2919 60 RL 74.0 9627 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 11 2006 WD Normal
1459 rows × 80 columns2.顺便删除无意义数据列
在每个样本中第一个特征是ID)这有助于模型识别每个训练样本。 虽然这很方便但它不携带任何用于预测的信息。 因此在将数据提供给模型之前将其从数据集中删除并且把训练集测试集拼接到一起、方便处理数据。
all_features pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))
all_featuresMSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig ... ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
0 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 2 2008 WD Normal
1 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub FR2 ... 0 0 NaN NaN NaN 0 5 2007 WD Normal
2 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 9 2008 WD Normal
3 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub Corner ... 0 0 NaN NaN NaN 0 2 2006 WD Abnorml
4 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub FR2 ... 0 0 NaN NaN NaN 0 12 2008 WD Normal
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 160 RM 21.0 1936 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 6 2006 WD Normal
1455 160 RM 21.0 1894 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 4 2006 WD Abnorml
1456 20 RL 160.0 20000 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 9 2006 WD Abnorml
1457 85 RL 62.0 10441 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN MnPrv Shed 700 7 2006 WD Normal
1458 60 RL 74.0 9627 Pave NaN Reg Lvl AllPub Inside ... 0 0 NaN NaN NaN 0 11 2006 WD Normal
2919 rows × 79 columns二、数据预处理
有各种各样的数据类型。 在开始建模之前需要对数据进行预处理。
1.特征缩放Z-score数据标准化
首先将所有缺失的值替换为相应特征的平均值。然后为了将所有特征放在一个共同的尺度上 通过将特征重新缩放到零均值和单位方差来标准化数据其中 和 分别表示均值和标准差。 标准化数据有两个原因 首先它方便优化。 其次因为不知道哪些特征是相关的 所以不想让惩罚分配给一个特征的系数比分配给其他任何特征的系数更大。
# 若无法获得测试数据则可根据训练数据计算均值和标准差
numeric_features all_features.dtypes[all_features.dtypes ! object].index
all_features[numeric_features] all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std()))常用的预处理方法将实值数据重新缩放为零均值和单位方法用前后一段时间的均值替换缺失值。
2.处理缺失值
# 在标准化数据之后所有均值消失因此我们可以将缺失值设置为0
all_features[numeric_features] all_features[numeric_features].fillna(0)3.处理离散值
处理离散值。这包括诸如“MSZoning”之类的特征。 用独热编码替换它们 方法与前面将多类别标签转换为向量的方式相同。
创建两个新的指示器特征“MSZoning_RL”和“MSZoning_RM”其值为0或1。 根据独热编码如果“MSZoning”的原始值为“RL” 则“MSZoning_RL”为1“MSZoning_RM”为0。 pandas软件包会自动实现这一点。
# “Dummy_naTrue”将“na”缺失值视为有效的特征值并为其创建指示符特征
all_features pd.get_dummies(all_features, dummy_naTrue)
all_features.shapeall_featuresMSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... SaleType_Oth SaleType_WD SaleType_nan SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial SaleCondition_nan
0 0.067320 -0.184443 -0.217841 0.646073 -0.507197 1.046078 0.896679 0.523038 0.580708 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
1 -0.873466 0.458096 -0.072032 -0.063174 2.187904 0.154737 -0.395536 -0.569893 1.177709 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
2 0.067320 -0.055935 0.137173 0.646073 -0.507197 0.980053 0.848819 0.333448 0.097840 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
3 0.302516 -0.398622 -0.078371 0.646073 -0.507197 -1.859033 -0.682695 -0.569893 -0.494771 -0.29303 ... 0 1 0 1 0 0 0 0 0 0
4 0.067320 0.629439 0.518814 1.355319 -0.507197 0.947040 0.753100 1.381770 0.468770 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2.419286 -2.069222 -1.043758 -1.481667 1.289537 -0.043338 -0.682695 -0.569893 -0.968860 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
1455 2.419286 -2.069222 -1.049083 -1.481667 -0.507197 -0.043338 -0.682695 -0.569893 -0.415757 -0.29303 ... 0 1 0 1 0 0 0 0 0 0
1456 -0.873466 3.884968 1.246594 -0.772420 1.289537 -0.373465 0.561660 -0.569893 1.717643 -0.29303 ... 0 1 0 1 0 0 0 0 0 0
1457 0.655311 -0.312950 0.034599 -0.772420 -0.507197 0.682939 0.370221 -0.569893 -0.229194 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
1458 0.067320 0.201080 -0.068608 0.646073 -0.507197 0.715952 0.465941 -0.045732 0.694840 -0.29303 ... 0 1 0 0 0 0 0 1 0 0
2919 rows × 331 columns转换会将特征的总数量从79个增加到331个。 最后通过values属性可以 从pandas格式中提取NumPy格式并将其转换为张量表示用于训练。
n_train train_data.shape[0]
train_features tf.constant(all_features[:n_train].values, dtypetf.float32)
test_features tf.constant(all_features[n_train:].values, dtypetf.float32)
train_labels tf.constant(train_data.SalePrice.values.reshape(-1, 1), dtypetf.float32)训练数据的特征内容如下不包括标签列
all_features[:n_train]MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... SaleType_Oth SaleType_WD SaleType_nan SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial SaleCondition_nan
0 0.067320 -0.184443 -0.217841 0.646073 -0.507197 1.046078 0.896679 0.523038 0.580708 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1 -0.873466 0.458096 -0.072032 -0.063174 2.187904 0.154737 -0.395536 -0.569893 1.177709 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
2 0.067320 -0.055935 0.137173 0.646073 -0.507197 0.980053 0.848819 0.333448 0.097840 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
3 0.302516 -0.398622 -0.078371 0.646073 -0.507197 -1.859033 -0.682695 -0.569893 -0.494771 -0.293030 ... 0 1 0 1 0 0 0 0 0 0
4 0.067320 0.629439 0.518814 1.355319 -0.507197 0.947040 0.753100 1.381770 0.468770 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 0.067320 -0.312950 -0.285421 -0.063174 -0.507197 0.914028 0.753100 -0.569893 -0.968860 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1456 -0.873466 0.672275 0.381246 -0.063174 0.391170 0.220763 0.178782 0.093673 0.765076 0.670295 ... 0 1 0 0 0 0 0 1 0 0
1457 0.302516 -0.141607 -0.142781 0.646073 3.086271 -1.000704 1.040259 -0.569893 -0.365275 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1458 -0.873466 -0.055935 -0.057197 -0.772420 0.391170 -0.703591 0.561660 -0.569893 -0.861312 5.788329 ... 0 1 0 0 0 0 0 1 0 0
1459 -0.873466 0.243916 -0.029303 -0.772420 0.391170 -0.208401 -0.921995 -0.569893 0.852870 1.420862 ... 0 1 0 0 0 0 0 1 0 0
1460 rows × 331 columns测试集数据的特征如下自然也是没有标签列
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... SaleType_Oth SaleType_WD SaleType_nan SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial SaleCondition_nan
0 -0.873466 0.458096 0.184340 -0.772420 0.391170 -0.340452 -1.113434 -0.569893 0.058332 0.558006 ... 0 1 0 0 0 0 0 1 0 0
1 -0.873466 0.500932 0.519702 -0.063174 0.391170 -0.439490 -1.257014 0.032335 1.056991 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
2 0.067320 0.201080 0.464294 -0.772420 -0.507197 0.848003 0.657380 -0.569893 0.767271 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
3 0.067320 0.372424 -0.024105 -0.063174 0.391170 0.881015 0.657380 -0.458369 0.352443 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
4 1.478499 -1.126832 -0.654636 1.355319 -0.507197 0.682939 0.370221 -0.569893 -0.391613 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2.419286 -2.069222 -1.043758 -1.481667 1.289537 -0.043338 -0.682695 -0.569893 -0.968860 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1455 2.419286 -2.069222 -1.049083 -1.481667 -0.507197 -0.043338 -0.682695 -0.569893 -0.415757 -0.293030 ... 0 1 0 1 0 0 0 0 0 0
1456 -0.873466 3.884968 1.246594 -0.772420 1.289537 -0.373465 0.561660 -0.569893 1.717643 -0.293030 ... 0 1 0 1 0 0 0 0 0 0
1457 0.655311 -0.312950 0.034599 -0.772420 -0.507197 0.682939 0.370221 -0.569893 -0.229194 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1458 0.067320 0.201080 -0.068608 0.646073 -0.507197 0.715952 0.465941 -0.045732 0.694840 -0.293030 ... 0 1 0 0 0 0 0 1 0 0
1459 rows × 331 columns训练数据的标签
train_data.SalePrice0 208500
1 181500
2 223500
3 140000
4 250000...
1455 175000
1456 210000
1457 266500
1458 142125
1459 147500
Name: SalePrice, Length: 1460, dtype: int64三、训练模型
训练一个带有损失平方的线性模型。
loss tf.keras.losses.MeanSquaredError()def get_net():net tf.keras.models.Sequential()net.add(tf.keras.layers.Dense(1, kernel_regularizertf.keras.regularizers.l2(weight_decay)))return net房价就像股票价格一样关心的是相对数量而不是绝对数量。 因此更关心相对误差(−̂)/ 而不是绝对误差−̂ 。 解决这个问题的一种方法是用价格预测的对数来衡量差异, 事实上这也是比赛中官方用来评价提交质量的误差指标。 即将 for |log−loĝ |≤ 转换为 (−)≤̂/≤() 。 这使得预测价格的对数与真实标签价格的对数之间出现以下均方根误差
def log_rmse(y_true, y_pred):# 为了在取对数时进一步稳定该值将小于1的值设置为1clipped_preds tf.clip_by_value(y_pred, 1, float(inf))return tf.sqrt(tf.reduce_mean(loss(tf.math.log(y_true), tf.math.log(clipped_preds))))训练函数将借助Adam优化器,Adam优化器的主要吸引力在于它对初始学习率不那么敏感。
def train(net, train_features, train_labels, test_features, test_labels,num_epochs, learning_rate, weight_decay, batch_size):train_ls, test_ls [], []train_iter d2l.load_array((train_features, train_labels), batch_size)# 这里使用的是Adam优化算法optimizer tf.keras.optimizers.Adam(learning_rate)net.compile(lossloss, optimizeroptimizer)for epoch in range(num_epochs):for X, y in train_iter:with tf.GradientTape() as tape:y_hat net(X)l loss(y, y_hat)params net.trainable_variablesgrads tape.gradient(l, params)optimizer.apply_gradients(zip(grads, params))train_ls.append(log_rmse(train_labels, net(train_features)))if test_labels is not None:test_ls.append(log_rmse(test_labels, net(test_features)))return train_ls, test_ls四、K折交叉验证
K折交叉验证 它有助于模型选择和超参数调整。首先需要定义一个函数在 折交叉验证过程中返回第 折的数据。具体地说它选择第 个切片作为验证数据其余部分作为训练数据。注意这并不是处理数据的最有效方法如果数据集大得多会有其他解决办法。
def get_k_fold_data(k, i, X, y):assert k 1fold_size X.shape[0] // kX_train, y_train None, Nonefor j in range(k):idx slice(j * fold_size, (j 1) * fold_size)X_part, y_part X[idx, :], y[idx]if j i:X_valid, y_valid X_part, y_partelif X_train is None:X_train, y_train X_part, y_partelse:X_train tf.concat([X_train, X_part], 0)y_train tf.concat([y_train, y_part], 0)return X_train, y_train, X_valid, y_valid在 折交叉验证中训练 次后返回训练和验证误差的平均值。
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,batch_size):train_l_sum, valid_l_sum 0, 0for i in range(k):data get_k_fold_data(k, i, X_train, y_train)net get_net()train_ls, valid_ls train(net, *data, num_epochs, learning_rate,weight_decay, batch_size)train_l_sum train_ls[-1]valid_l_sum valid_ls[-1]if i 0:d2l.plot(list(range(1, num_epochs 1)), [train_ls, valid_ls],xlabelepoch, ylabelrmse, xlim[1, num_epochs],legend[train, valid], yscalelog)print(f折{i 1}训练log rmse{float(train_ls[-1]):f}, f验证log rmse{float(valid_ls[-1]):f})return train_l_sum / k, valid_l_sum / k五、模型选择
选择了一组未调优的超参数并将其留给读者来改进模型。 找到一组调优的超参数可能需要时间这取决于一个人优化了多少变量。
有了足够大的数据集和合理设置的超参数 折交叉验证往往对多次测试具有相当的稳定性。 然而如果尝试了不合理的超参数可能会发现验证效果不再代表真正的误差。
k, num_epochs, lr, weight_decay, batch_size 5, 100, 5, 0, 64
train_l, valid_l k_fold(k, train_features, train_labels, num_epochs, lr,weight_decay, batch_size)
print(f{k}-折验证: 平均训练log rmse: {float(train_l):f}, f平均验证log rmse: {float(valid_l):f})折1训练log rmse0.170193, 验证log rmse0.157399
折2训练log rmse0.162299, 验证log rmse0.189597
折3训练log rmse0.164040, 验证log rmse0.167844
折4训练log rmse0.168239, 验证log rmse0.154891
折5训练log rmse0.163944, 验证log rmse0.182911
5-折验证: 平均训练log rmse: 0.165743, 平均验证log rmse: 0.170528六、保存预测数据
既然知道应该选择什么样的超参数 不妨使用所有数据对其进行训练 而不是仅使用交叉验证中使用的 1−1/的数据。 然后通过这种方式获得的模型可以应用于测试集。 将预测保存在CSV文件中。
def train_and_pred(train_features, test_features, train_labels, test_data,num_epochs, lr, weight_decay, batch_size):net get_net()#训练网络train_ls, _ train(net, train_features, train_labels, None, None,num_epochs, lr, weight_decay, batch_size)# 可视化损失变化d2l.plot(np.arange(1, num_epochs 1), [train_ls], xlabelepoch,ylabellog rmse, xlim[1, num_epochs], yscalelog)print(f训练log rmse{float(train_ls[-1]):f})# 将网络应用于测试集。preds net(test_features).numpy()# 将其重新格式化以导出到Kaggletest_data[SalePrice] pd.Series(preds.reshape(1, -1)[0])submission pd.concat([test_data[Id], test_data[SalePrice]], axis1)submission.to_csv(submission.csv, indexFalse)train_and_pred(train_features, test_features, train_labels, test_data,num_epochs, lr, weight_decay, batch_size)
